The Center for Cancer Systems Pharmacology

Research

Data Standards and Research Reproducibility

The LSP is strongly committed to FAIR (Findable, Accessible, Interoperable and Reusable) research.

We thoughtfully consider the factors that influence the reproducibility of laboratory-based research findings and advocate for solutions. Whenever feasible, LSP results and software are open-source, and data is available under public domain (i.e., Creative Commons) licenses. We also run a seminar series that features speakers at the cutting edge of data and knowledge management. 

LSP investigators have studied the irreproducibility of preclinical drug response and pharmacodynamic data in detail (Niepel, 2019) and developed multiple methods to address the problem (Hafner, 2016; Mills, 2022), commented on the importance of public data release for reproducibility (AlQuraishi, 2016), and developed methods to liberate survival data about from clinical trials from pictorial representations (Plana, 2022).

In the emerging field of multiplexed imaging, we have created open-source data processing pipelines to increase the reliability of complex data analysis (Schapiro, 2022a), established a metadata scheme for standardizing the description of tissue images (Schapiro, 2022b), and developed the first quality control software for high-plex imaging data (Baker, 2024). 

We also developed Minerva, a lightweight software that makes it possible to share whole slide multiplex images online without download (Hoffer, 2020; Rashid, 2022).  We've partnered with external organizations like cBioPortal (Wala, 2024) and the Data Coordinating Center (DCC) of the Human Tumor Atlas Network (HTAN) (De Bruijn, 2024) to enable large scale tissue atlases by incorporating the Minerva image viewer into existing data repositories. 

The Minerva image viewer
A sample of metastatic melanoma as viewed through Minerva. A subset of channels are shown. See https://www.cycif.org/data/MIP-CHTN to browse the image.

 

Relevant publications:

1.
Baker GJ, Novikov E, Zhao Z, et al. Quality control for single-cell analysis of high-plex tissue profiles using CyLinter. Nat Methods. Published online October 30, 2024. http://doi.org/10.1038/s41592-024-02328-0
1.
Wala J, de Bruijn I, Coy S, et al. Integrating spatial profiles and cancer genomics to identify immune-infiltrated mismatch repair proficient colorectal cancers. Published online September 26, 2024. http://doi.org/10.1101/2024.09.24.614701
1.
De Bruijn I, Nikolov M, Lau C, et al. Sharing Data from the Human Tumor Atlas Network through Standards, Infrastructure, and Community Engagement. Published online June 30, 2024. http://doi.org/10.1101/2024.06.25.598921
1.
Mills CE, Subramanian K, Hafner M, et al. Multiplexed and reproducible high content screening of live and fixed cells using Dye Drop. Nat Commun. 2022;13(1):6918. http://doi.org/10.1038/s41467-022-34536-7
1.
Rashid R, Chen YA, Hoffer J, et al. Narrative online guides for the interpretation of digital-pathology images and tissue-atlas data. Nat Biomed Eng. 2022;6(5):515-526. http://doi.org/10.1038/s41551-021-00789-8
1.
Schapiro D, Yapp C, Sokolov A, et al. MITI minimum information guidelines for highly multiplexed tissue images. Nat Methods. 2022;19(3):262-267. http://doi.org/10.1038/s41592-022-01415-4
1.
Schapiro D, Sokolov A, Yapp C, et al. MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging. Nat Methods. 2022;19(3):311-315. http://doi.org/10.1038/s41592-021-01308-y
1.
Plana D, Fell G, Alexander BM, Palmer AC, Sorger PK. Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects. Nat Commun. 2022;13(1):873. http://doi.org/10.1038/s41467-022-28410-9
1.
Hoffer J, Rashid R, Muhlich JL, et al. Minerva: a light-weight, narrative image browser for multiplexed tissue images. J Open Source Softw. 2020;5(54):2579. http://doi.org/10.21105/joss.02579
1.
Niepel M, Hafner M, Mills CE, et al. A multi-center study on the reproducibility of drug-response assays in mammalian cell lines. Cell Syst. 2019;9(1):35-48.e5. http://doi.org/10.1016/j.cels.2019.06.005
1.
Hafner M, Niepel M, Chung M, Sorger PK. Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nat Methods. 2016;13(6):521-527. http://doi.org/10.1038/nmeth.3853
1.
AlQuraishi M, Sorger PK. Reproducibility will only come with data liberation. Sci Transl Med. 2016;8(339):339ed7. http://doi.org/10.1126/scitranslmed.aaf0968